

import tempfile
import unittest

from make_student import create_student_by_copying_alternating_layers

from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch


lowerCAmelCase_			      =      '''sshleifer/bart-tiny-random'''
lowerCAmelCase_			      =      '''patrickvonplaten/t5-tiny-random'''





@require_torch
class       snake_case_  (						unittest.TestCase   ):







			'''simple docstring'''

			@cached_property
			def 							snake_case__(	self					:							List[str]				) ->Union[str, Any]:
									return AutoConfig.from_pretrained(_UpperCamelCase				)







			def 							snake_case__(	self					:							List[str]				) ->str:
									snake_case_,       *snake_case_  =      create_student_by_copying_alternating_layers(_UpperCamelCase				,  tempfile.mkdtemp()				,  e=1				,  d=1				)
									self.assertEqual(student.config.num_hidden_layers				,  1				)







			def 							snake_case__(	self					:							List[Any]				) ->Any:
									snake_case_,       *snake_case_  =      create_student_by_copying_alternating_layers(_UpperCamelCase				,  tempfile.mkdtemp()				,  e=1				,  d=_UpperCamelCase				)







			def 							snake_case__(	self					:							List[str]				) ->str:
									snake_case_,       *snake_case_  =      create_student_by_copying_alternating_layers(_UpperCamelCase				,  tempfile.mkdtemp()				,  e=1				,  d=_UpperCamelCase				)
									self.assertEqual(student.config.encoder_layers				,  1				)
									self.assertEqual(student.config.decoder_layers				,  self.teacher_config.encoder_layers				)







			def 							snake_case__(	self					:							List[Any]				) ->int:
									snake_case_,       *snake_case_  =      create_student_by_copying_alternating_layers(_UpperCamelCase				,  tempfile.mkdtemp()				,  e=1				,  d=1				)
									self.assertEqual(student.config.encoder_layers				,  1				)
									self.assertEqual(student.config.decoder_layers				,  1				)







			def 							snake_case__(	self					:							Tuple				) ->Any:
									with self.assertRaises(_UpperCamelCase				):
															create_student_by_copying_alternating_layers(_UpperCamelCase				,  tempfile.mkdtemp()				,  e=_UpperCamelCase				,  d=_UpperCamelCase				)



'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_						:  int				  =						{'configuration_wavlm': ['WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'WavLMConfig']}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase_						:  Tuple				  =						[
				    'WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'WavLMForAudioFrameClassification',
				    'WavLMForCTC',
				    'WavLMForSequenceClassification',
				    'WavLMForXVector',
				    'WavLMModel',
				    'WavLMPreTrainedModel',
				]

if TYPE_CHECKING:
				from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_wavlm import (
								    WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
								    WavLMForAudioFrameClassification,
								    WavLMForCTC,
								    WavLMForSequenceClassification,
								    WavLMForXVector,
								    WavLMModel,
								    WavLMPreTrainedModel,
								)

else:
				import sys

				UpperCAmelCase_						:  Any				  =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




